Article

ABSTRACT. Recent years have witnessed the popularity of online Peer-to-Peer (P2P) lending which is regarded an alternative to banks where individual members lend and borrow money using an online trading platform without official financial intermediaries involved. The paper studies P2P lending and the determinants of Non-performing Loans (NPLs).
Compared with traditional loans, lenders in P2P should bear higher credit risks because of information asymmetry since they are at a disadvantage position facing the borrowers. NPLs are the direct financial lost for borrowers. It is important to clarify the key factors of NPLs for the lenders and establish a reliable model to judge whether a loan will turn to be NPL or Not based on the published information in the platform. The paper studies 9,276 transaction data of 2015 from PPDai, a leading P2P platform of China. A Non-parametric Random Forest (RF) model which has superiority in dealing with complex multidimensional data set and higher predicts accuracy has been applied to determine the factors of NPLs. A binary logistic regression model is also generated to quantify the possibility of NPLs based on the selected indicator through RF. According to the result, the times of payment are the most predictive factor to NPLs.